Changes for page SDMX 2.1 Standards. Section 6. Technical Notes
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... ... @@ -13,8 +13,10 @@ 13 13 14 14 == 1.1 Purpose == 15 15 16 -The intention of this document is to document certain aspects of SDMX that are important to understand and will aid implementation decisions. The explanations here supplement the information documented in the SDMX XML schema and the Information Model.16 +The intention of this document is to document certain aspects of SDMX that are important to understand and will aid implementation decisions. The explanations here supplement the information documented in the SDMX XML schema and the 17 17 18 +Information Model. 19 + 18 18 == 1.2 Structure == 19 19 20 20 This document is organized into the following major parts: ... ... @@ -39,7 +39,7 @@ 39 39 40 40 == 3.2 SDMX Information Model for Format Implementers == 41 41 42 -=== 3.2.1 Introduction === 44 +=== 3.2.1 Introduction === 43 43 44 44 The purpose of this sub-section is to provide an introduction to the SDMX-IM relating to Data Structure Definitions and Data Sets for those whose primary interest is in the use of the XML or EDI formats. For those wishing to have a deeper understanding of the Information Model, the full SDMX-IM document, and other sections in this guide provide a more in-depth view, along with UML diagrams and supporting explanation. For those who are unfamiliar with DSDs, an appendix to the SDMX-IM provides a tutorial which may serve as a useful introduction. 45 45 ... ... @@ -47,12 +47,16 @@ 47 47 48 48 The Data Structure Definition and Data Set parts of the information model are consistent with the GESMES/TS version 3.0 Data Model (called SDMX-EDI in the SDMX standard), with these exceptions: 49 49 50 -* the “sibling group” construct has been generalized to permit any dimension or dimensions to be wildcarded, and not just frequency, as in GESMES/TS. It has been renamed a “group” to distinguish it from the “sibling group” where only frequency is wildcarded. The set of allowable partial “group” keys must be declared in the DSD, and attributes may be attached to any of these group keys; 51 -* furthermore, whilst the “group” has been retained for compatibility with version 2.0 and with SDMX-EDI, it has, at version 2.1, been replaced by the “Attribute Relationship” definition which is explained later 52 -* the section on data representation is now a convention, to support interoperability with EDIFACT-syntax implementations ( see section 3.3.2); 52 +the “sibling group” construct has been generalized to permit any dimension or dimensions to be wildcarded, and not just frequency, as in GESMES/TS. It has been renamed a “group” to distinguish it from the “sibling group” where only frequency is wildcarded. The set of allowable partial “group” keys must be declared in the DSD, and attributes may be attached to any of these group keys; 53 53 54 - DSD-specific data formats arederived fromthe model,andsome supportingfeaturesfor declaringmultiplemeasureshavebeen addedtothe structuralmetadata descriptions Clearly,thisisnotacoincidence.TheGESMES/TSDataModel providesthefoundationfortheEDIFACT messages inSDMX-EDI,andalsoisthestartingpointforthedevelopmentof SDMX-ML.54 +furthermore, whilst the “group” has been retained for compatibility with version 2.0 and with SDMX-EDI, it has, at version 2.1, been replaced by the “Attribute Relationship” definition which is explained later 55 55 56 +the section on data representation is now a convention, to support interoperability with EDIFACT-syntax implementations ( see section 3.3.2); 57 + 58 +DSD-specific data formats are derived from the model, and some supporting features for declaring multiple measures have been added to the structural metadata descriptions 59 + 60 +Clearly, this is not a coincidence. The GESMES/TS Data Model provides the foundation for the EDIFACT messages in SDMX-EDI, and also is the starting point for the development of SDMX-ML. 61 + 56 56 Note that in the descriptions below, text in courier and italicised are the names used in the information model (e.g. //DataSet//). 57 57 58 58 == 3.3 SDMX-ML and SDMX-EDI: Comparison of Expressive Capabilities and Function == ... ... @@ -59,16 +59,22 @@ 59 59 60 60 SDMX offers several equivalent formats for describing data and structural metadata, optimized for use in different applications. Although all of these formats are derived directly from the SDM-IM, and are thus equivalent, the syntaxes used to express the model place some restrictions on their use. Also, different optimizations provide different capabilities. This section describes these differences, and provides some rules for applications which may need to support more than one SDMX format or syntax. This section is constrained to the Data Structure Definitionand the Date Set. 61 61 62 -=== 3.3.1 Format Optimizations and Differences === 68 +=== 3.3.1 Format Optimizations and Differences === 63 63 64 64 The following section provides a brief overview of the differences between the various SDMX formats. 65 65 66 -Version 2.0 was characterised by 4 data messages, each with a distinct format: Generic, Compact, Cross-Sectional and Utility. Because of the design, data in some formats could not always be related to another format. In version 2.1, this issue has been addressed by merging some formats and eliminating others. As a result, in SDMX 2.1 there are just two types of data formats: //GenericData// and //StructureSpecificData// (i.e. specific to one Data Structure Definition).72 +Version 2.0 was characterised by 4 data messages, each with a distinct format: Generic, Compact, Cross-Sectional and Utility. Because of the design, data in some formats could not always be related to another format. In version 2.1, this issue has been addressed by merging some formats and eliminating others. As a result, in 67 67 74 +SDMX 2.1 there are just two types of data formats: //GenericData// and 75 + 76 +//StructureSpecificData// (i.e. specific to one Data Structure Definition). 77 + 68 68 Both of these formats are now flexible enough to allow for data to be oriented in series with any dimension used to disambiguate the observations (as opposed to only time or a cross sectional measure in version 2.0). The formats have also been expanded to allow for ungrouped observations. 69 69 70 -To allow for applications which only understand time series data, variations of these formats have been introduced in the form of two data messages; //GenericTimeSeriesData// and //StructureSpecificTimeSeriesData//. It is important to note that these variations are built on the same root structure and can be processed in the same manner as the base format so that they do NOT introduce additional processing requirements.80 +To allow for applications which only understand time series data, variations of these formats have been introduced in the form of two data messages; 71 71 82 +//GenericTimeSeriesData// and //StructureSpecificTimeSeriesData//. It is important to note that these variations are built on the same root structure and can be processed in the same manner as the base format so that they do NOT introduce additional processing requirements. 83 + 72 72 === //Structure Definition// === 73 73 74 74 The SDMX-ML Structure Message supports the use of annotations to the structure, which is not supported by the SDMX-EDI syntax. ... ... @@ -77,8 +77,10 @@ 77 77 78 78 === //Validation// === 79 79 80 -SDMX-EDI – as is typical of EDIFACT syntax messages – leaves validation to dedicated applications (“validation” being the checking of syntax, data typing, and adherence of the data message to the structure as described in the structural definition.)92 +SDMX-EDI – as is typical of EDIFACT syntax messages – leaves validation to dedicated applications (“validation” being the checking of syntax, data typing, and adherence of the data message to the structure as described in the structural 81 81 94 +definition.) 95 + 82 82 The SDMX-ML Generic Data Message also leaves validation above the XML syntax level to the application. 83 83 84 84 The SDMX-ML DSD-specific messages will allow validation of XML syntax and datatyping to be performed with a generic XML parser, and enforce agreement between the structural definition and the data to a moderate degree with the same tool. ... ... @@ -89,13 +89,17 @@ 89 89 90 90 === //Character Encodings// === 91 91 92 -All SDMX-ML messages use the UTF-8 encoding, while SDMX-EDI uses the ISO 8879-1 character encoding. There is a greater capacity with UTF-8 to express some character sets (see the “APPENDIX: MAP OF ISO 8859-1 (UNOC) CHARACTER SET (LATIN 1 OR “WESTERN”) in the document “SYNTAX AND DOCUMENTATION VERSION 2.0”.) Many transformation tools are available which allow XML instances with UTF-8 encodings to be expressed as ISO 8879-1-encoded characters, and to transform UTF-8 into ISO 8879-1. Such tools should be used when transforming SDMX-ML messages into SDMX-EDI messages and vice-versa.106 +All SDMX-ML messages use the UTF-8 encoding, while SDMX-EDI uses the ISO 8879-1 character encoding. There is a greater capacity with UTF-8 to express some character sets (see the “APPENDIX: MAP OF ISO 8859-1 (UNOC) CHARACTER 93 93 108 +SET (LATIN 1 OR “WESTERN”) in the document “SYNTAX AND 109 + 110 +DOCUMENTATION VERSION 2.0”.) Many transformation tools are available which allow XML instances with UTF-8 encodings to be expressed as ISO 8879-1-encoded characters, and to transform UTF-8 into ISO 8879-1. Such tools should be used when transforming SDMX-ML messages into SDMX-EDI messages and vice-versa. 111 + 94 94 === //Data Typing// === 95 95 96 96 The XML syntax and EDIFACT syntax have different data-typing mechanisms. The section below provides a set of conventions to be observed when support for messages in both syntaxes is required. For more information on the SDMX-ML representations of data, see below. 97 97 98 -==== 3.3.2 Data Types ==== 116 +==== 3.3.2 Data Types ==== 99 99 100 100 The XML syntax has a very different mechanism for data-typing than the EDIFACT syntax, and this difference may create some difficulties for applications which support both EDIFACT-based and XML-based SDMX data formats. This section provides a set of conventions for the expression in data in all formats, to allow for clean interoperability between them. 101 101 ... ... @@ -111,8 +111,7 @@ 111 111 1*. Maximum 70 characters. 112 112 1*. From ISO 8859-1 character set (including accented characters) 113 113 1. **Descriptions **are: 114 -1*. Maximum 350 characters; 115 -1*. From ISO 8859-1 character set. 132 +1*. Maximum 350 characters; From ISO 8859-1 character set. 116 116 1. **Code values** are: 117 117 1*. Maximum 18 characters; 118 118 1*. Any of A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore), / (solidus, slash), = (equal sign), - (hyphen); ... ... @@ -121,27 +121,24 @@ 121 121 122 122 A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore) 123 123 124 -**5. Observation values** are: 141 +1. **Observation values** are: 142 +1*. Decimal numerics (signed only if they are negative); 143 +1*. The maximum number of significant figures is: 144 +1*. 15 for a positive number 145 +1*. 14 for a positive decimal or a negative integer 146 +1*. 13 for a negative decimal 147 +1*. Scientific notation may be used. 148 +1. **Uncoded statistical concept** text values are: 149 +1*. 150 +1**. Maximum 1050 characters; 151 +1**. From ISO 8859-1 character set. 152 +1. **Time series keys**: 125 125 126 -* Decimal numerics (signed only if they are negative); 127 -* The maximum number of significant figures is: 128 -* 15 for a positive number 129 -* 14 for a positive decimal or a negative integer 130 -* 13 for a negative decimal 131 -* Scientific notation may be used. 154 +In principle, the maximum permissible length of time series keys used in a data exchange does not need to be restricted. However, for working purposes, an effort is made to limit the maximum length to 35 characters; in this length, also (for SDMXEDI) one (separator) position is included between all successive dimension values; this means that the maximum length allowed for a pure series key (concatenation of dimension values) can be less than 35 characters. The separator character is a colon (“:”) by conventional usage. 132 132 133 -**6. Uncoded statistical concept** text values are: 134 - 135 -* Maximum 1050 characters; 136 -* From ISO 8859-1 character set. 137 - 138 -**7. Time series keys**: 139 - 140 -In principle, the maximum permissible length of time series keys used in a data exchange does not need to be restricted. However, for working purposes, an effort is made to limit the maximum length to 35 characters; in this length, also (for SDMXEDI) one (separator) position is included between all successive dimension values; this means that the maximum length allowed for a pure series key (concatenation of dimension values) can be less than 35 characters. The separator character is a colon (“:”) by conventional usage. 141 - 142 142 == 3.4 SDMX-ML and SDMX-EDI Best Practices == 143 143 144 -=== 3.4.1 Reporting and Dissemination Guidelines === 158 +=== 3.4.1 Reporting and Dissemination Guidelines === 145 145 146 146 **3.4.1.1 Central Institutions and Their Role in Statistical Data Exchanges **Central institutions are the organisations to which other partner institutions "report" statistics. These statistics are used by central institutions either to compile aggregates and/or they are put together and made available in a uniform manner (e.g. on-line or on a CD-ROM or through file transfers). Therefore, central institutions receive data from other institutions and, usually, they also "disseminate" data to individual and/or institutions for end-use. Within a country, a NSI or a national central bank (NCB) plays, of course, a central institution role as it collects data from other entities and it disseminates statistical information to end users. In SDMX the role of central institution is very important: every statistical message is based on underlying structural definitions (statistical concepts, code lists, DSDs) which have been devised by a particular agency, usually a central institution. Such an institution plays the role of the reference "structural definitions maintenance agency" for the corresponding messages which are exchanged. Of course, two institutions could exchange data using/referring to structural information devised by a third institution. 147 147 ... ... @@ -221,12 +221,10 @@ 221 221 * its content and description 222 222 * the relevant DSD that defines the structure of the data reported or disseminated according the the dataflow definition 223 223 224 - ====3.4.1.3 Exchanging Attributes====238 +**3.4.1.3 Exchanging Attributes** 225 225 226 - =====//3.4.1.3.1 Attributes on series, sibling and data set level //=====240 +**//3.4.1.3.1 Attributes on series, sibling and data set level //**//Static properties//. 227 227 228 -//Static properties//. 229 - 230 230 * Upon creation of a series the sender has to provide to the receiver values for all mandatory attributes. In case they are available, values for conditional attributes should also be provided. Whereas initially this information may be provided by means other than SDMX-ML or SDMX-EDI messages (e.g. paper, telephone) it is expected that partner institutions will be in a position to provide this information in SDMX-ML or SDMX-EDI format over time. 231 231 * A centre may agree with its data exchange partners special procedures for authorising the setting of attributes' initial values. 232 232 * Attribute values at a data set level are set and maintained exclusively by the centre administrating the exchanged data set. ... ... @@ -419,7 +419,7 @@ 419 419 420 420 This is used to unambiguously state that a date-time represents an observation at a single point in time. Therefore, if one wants to use SDMX for data which is measured at a distinct point in time rather than being reported over a period, the date-time representation can be used. 421 421 422 -Representation: xs:dateTime (YYYY-MM-DDThh:mm:ss)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[1~]^^>>path:#_ftn1]]434 +Representation: xs:dateTime (YYYY-MM-DDThh:mm:ss)[[(% class="wikiinternallink wikiinternallink" %)^^~[1~]^^>>path:#_ftn1]] 423 423 424 424 ==== 4.2.6 Standard Reporting Period ==== 425 425 ... ... @@ -487,7 +487,7 @@ 487 487 488 488 Representation: common:ReportingWeekType (YYYY-Www, e.g. 2000-W53) 489 489 490 -Notes: There are either 52 or 53 weeks in a reporting year. This is based on the ISO 8601 definition of a week (Monday - Saturday), where the first week of a reporting year is defined as the week with the first Thursday on or after the reporting year start day.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[2~]^^>>path:#_ftn2]](%%) The reporting week is always represented as two digits, therefore 1-9 are 0 padded (e.g. 01). This allows the values to be sorted chronologically using textual sorting methods.502 +Notes: There are either 52 or 53 weeks in a reporting year. This is based on the ISO 8601 definition of a week (Monday - Saturday), where the first week of a reporting year is defined as the week with the first Thursday on or after the reporting year start day.[[(% class="wikiinternallink wikiinternallink" %)^^~[2~]^^>>path:#_ftn2]](%%) The reporting week is always represented as two digits, therefore 1-9 are 0 padded (e.g. 01). This allows the values to be sorted chronologically using textual sorting methods. 491 491 492 492 **Reporting Day**: 493 493 ... ... @@ -544,7 +544,7 @@ 544 544 111. If the [PERIOD_INDICATOR] is D, the [PERIOD_DURATION] is P1D. 545 545 1. **Determine [PERIOD_START]:** 546 546 547 -Subtract one from the [PERIOD_VALUE] and multiply this by the [PERIOD_DURATION]. Add[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[3~]^^>>path:#_ftn3]](%%) this to the [REPORTING_YEAR_BASE]. The result is the [PERIOD_START].559 +Subtract one from the [PERIOD_VALUE] and multiply this by the [PERIOD_DURATION]. Add[[(% class="wikiinternallink wikiinternallink" %)^^~[3~]^^>>path:#_ftn3]](%%) this to the [REPORTING_YEAR_BASE]. The result is the [PERIOD_START]. 548 548 549 549 1. **Determine the [PERIOD_END]:** 550 550 ... ... @@ -1240,7 +1240,7 @@ 1240 1240 1241 1241 == 10.1 Introduction == 1242 1242 1243 -The Validation and Transformation Language (VTL) supports the definition of Transformations, which are algorithms to calculate new data starting from already existing ones[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[4~]^^>>path:#_ftn4]](%%). The purpose of the VTL in the SDMX context is to enable the:1255 +The Validation and Transformation Language (VTL) supports the definition of Transformations, which are algorithms to calculate new data starting from already existing ones[[(% class="wikiinternallink wikiinternallink" %)^^~[4~]^^>>path:#_ftn4]](%%). The purpose of the VTL in the SDMX context is to enable the: 1244 1244 1245 1245 * definition of validation and transformation algorithms, in order to specify how to calculate new data from existing ones; 1246 1246 * exchange of the definition of VTL algorithms, also together the definition of the data structures of the involved data (for example, exchange the data structures of a reporting framework together with the validation rules to be applied, exchange the input and output data structures of a calculation task together with the VTL Transformations describing the calculation algorithms); ... ... @@ -1264,7 +1264,7 @@ 1264 1264 1265 1265 In any case, the aliases used in the VTL transformations have to be mapped to the 1266 1266 1267 -SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL transformations, rulesets[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[5~]^^>>path:#_ftn5]](%%) or user defined operators[[(% class="wikiinternallink wikiinternallinkwikiinternallink wikiinternallink" %)^^~[6~]^^>>path:#_ftn6]](%%) to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.1279 +SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL transformations, rulesets[[(% class="wikiinternallink wikiinternallink" %)^^~[5~]^^>>path:#_ftn5]](%%) or user defined operators[[(% class="wikiinternallink wikiinternallink" %)^^~[6~]^^>>path:#_ftn6]](%%) to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping. 1268 1268 1269 1269 The correspondence between an alias and a SDMX artefact must be one-to-one, meaning that a generic alias identifies one and just one SDMX artefact while a SDMX artefact is identified by one and just one alias. In other words, within a VtlMappingScheme an artefact can have just one alias and different artefacts cannot have the same alias. 1270 1270 ... ... @@ -1274,7 +1274,7 @@ 1274 1274 1275 1275 This approach has the advantage that in the VTL code the URN of the referenced artefacts is directly intelligible by a human reader but has the drawback that the references are verbose. 1276 1276 1277 -The SDMX URN[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[7~]^^>>path:#_ftn7]](%%) is the concatenation of the following parts, separated by special symbols like dot, equal, asterisk, comma, and parenthesis:^^ ^^1289 +The SDMX URN[[(% class="wikiinternallink wikiinternallink" %)^^~[7~]^^>>path:#_ftn7]](%%) is the concatenation of the following parts, separated by special symbols like dot, equal, asterisk, comma, and parenthesis:^^ ^^ 1278 1278 1279 1279 * SDMXprefix 1280 1280 * SDMX-IM-package-name ... ... @@ -1282,7 +1282,7 @@ 1282 1282 * agency-id 1283 1283 * maintainedobject-id 1284 1284 * maintainedobject-version 1285 -* container-object-id [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[8~]^^>>path:#_ftn8]]1297 +* container-object-id [[(% class="wikiinternallink wikiinternallink" %)^^~[8~]^^>>path:#_ftn8]] 1286 1286 * object-id 1287 1287 1288 1288 The generic structure of the URN is the following: ... ... @@ -1301,7 +1301,7 @@ 1301 1301 1302 1302 The **agency-id** is the acronym of the agency that owns the definition of the artefact, for example for the Eurostat artefacts the agency-id is “ESTAT”). The agency-id can be composite (for example AgencyA.Dept1.Unit2). 1303 1303 1304 -The **maintainedobject-id** is the name of the maintained object which the artefact belongs to, and in case the artefact itself is maintainable[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[9~]^^>>path:#_ftn9]](%%), coincides with the name of the artefact. Therefore the maintainedobject-id depends on the class of the artefact:1316 +The **maintainedobject-id** is the name of the maintained object which the artefact belongs to, and in case the artefact itself is maintainable[[(% class="wikiinternallink wikiinternallink" %)^^~[9~]^^>>path:#_ftn9]](%%), coincides with the name of the artefact. Therefore the maintainedobject-id depends on the class of the artefact: 1305 1305 1306 1306 * if the artefact is a ,,Dataflow,,, which is a maintainable class, the maintainedobject-id is the Dataflow name (dataflow-id); 1307 1307 * if the artefact is a Dimension, MeasureDimension, TimeDimension, PrimaryMeasure or DataAttribute, which are not maintainable and belong to the ,,DataStructure,, maintainable class, the maintainedobject-id is the name of the DataStructure (dataStructure-id) which the artefact belongs to; ... ... @@ -1321,7 +1321,7 @@ 1321 1321 1322 1322 * if the artefact is a ,,Concept ,,(the object-id is the name of the ,,Concept,,) 1323 1323 1324 -For example, by using the URN, the VTL transformation that sums two SDMX dataflows DF1 and DF2 and assigns the result to a third persistent dataflow DFR, assuming that DF1, DF2 and DFR are the maintainedobject-id of the three dataflows, that their version is 1.0 and their Agency is AG, would be written as[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[10~]^^>>path:#_ftn10]](%%):1336 +For example, by using the URN, the VTL transformation that sums two SDMX dataflows DF1 and DF2 and assigns the result to a third persistent dataflow DFR, assuming that DF1, DF2 and DFR are the maintainedobject-id of the three dataflows, that their version is 1.0 and their Agency is AG, would be written as[[(% class="wikiinternallink wikiinternallink" %)^^~[10~]^^>>path:#_ftn10]](%%): 1325 1325 1326 1326 ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0)’ <- 1327 1327 ... ... @@ -1339,8 +1339,8 @@ 1339 1339 * The **SDMX-IM-package-name **can be omitted as well because it can be deduced from the class-name that follows it (the table of the SDMX-IM packages and classes that allows this deduction is in the SDMX 2.1 Standards - Section 5 - Registry Specifications, paragraph 6.2.3). In particular, considering the object classes of the artefacts that VTL can reference, the package is: 1340 1340 ** “datastructure” for the classes Dataflow, Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute, 1341 1341 ** “conceptscheme” for the classes Concept and ConceptScheme o “codelist” for the class Codelist. 1342 -* The **class-name** can be omitted as it can be deduced from the VTL invocation. In particular, starting from the VTL class of the invoked artefact (e.g. dataset, component, identifier, measure, attribute, variable, valuedomain), which is known given the syntax of the invoking VTL operator[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[11~]^^>>path:#_ftn11]](%%), the SDMX class can be deduced from the mapping rules between VTL and SDMX (see the section “Mapping between VTL and SDMX” hereinafter)[[(% class="wikiinternallink wikiinternallinkwikiinternallink wikiinternallink" %)^^~[12~]^^>>path:#_ftn12]](%%).1343 -* If the **agency-id** is not specified, it is assumed by default equal to the agency-id of the TransformationScheme, UserDefinedOperatorScheme or RulesetScheme from which the artefact is invoked. For example, the agency-id can be omitted if it is the same as the invoking T,,ransformationScheme,, and cannot be omitted if the artefact comes from another agency.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[13~]^^>>path:#_ftn13]](%%) Take also into account that, according to the VTL consistency rules, the agency of the result of a ,,Transformation,, must be the same as its ,,TransformationScheme,,, therefore the agency-id can be omitted for all the results (left part of ,,Transformation,, statements).1354 +* The **class-name** can be omitted as it can be deduced from the VTL invocation. In particular, starting from the VTL class of the invoked artefact (e.g. dataset, component, identifier, measure, attribute, variable, valuedomain), which is known given the syntax of the invoking VTL operator[[(% class="wikiinternallink wikiinternallink" %)^^~[11~]^^>>path:#_ftn11]](%%), the SDMX class can be deduced from the mapping rules between VTL and SDMX (see the section “Mapping between VTL and SDMX” hereinafter)[[(% class="wikiinternallink wikiinternallink" %)^^~[12~]^^>>path:#_ftn12]](%%). 1355 +* If the **agency-id** is not specified, it is assumed by default equal to the agency-id of the TransformationScheme, UserDefinedOperatorScheme or RulesetScheme from which the artefact is invoked. For example, the agency-id can be omitted if it is the same as the invoking T,,ransformationScheme,, and cannot be omitted if the artefact comes from another agency.[[(% class="wikiinternallink wikiinternallink" %)^^~[13~]^^>>path:#_ftn13]](%%) Take also into account that, according to the VTL consistency rules, the agency of the result of a ,,Transformation,, must be the same as its ,,TransformationScheme,,, therefore the agency-id can be omitted for all the results (left part of ,,Transformation,, statements). 1344 1344 * As for the **maintainedobject-id**, this is essential in some cases while in other cases it can be omitted: o if the referenced artefact is a ,,Dataflow,,, which is a maintainable class, the maintainedobject-id is the dataflow-id and obviously cannot be omitted; 1345 1345 ** if the referenced artefact is a Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute, which are not maintainable and belong to the ,,DataStructure,, maintainable class, the maintainedobject-id is the dataStructure-id and can be omitted, given that these components are always invoked within the invocation of a ,,Dataflow,,, whose dataStructure-id can be deduced from the 1346 1346 ... ... @@ -1367,11 +1367,11 @@ 1367 1367 1368 1368 DFR := DF1 + DF2 1369 1369 1370 -The references to the ,,Codelists,, can be simplified similarly. For example, given the non-abbreviated reference to the ,,Codelist,, AG:CL_FREQ(1.0), which is[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[14~]^^>>path:#_ftn14]](%%):1382 +The references to the ,,Codelists,, can be simplified similarly. For example, given the non-abbreviated reference to the ,,Codelist,, AG:CL_FREQ(1.0), which is[[(% class="wikiinternallink wikiinternallink" %)^^~[14~]^^>>path:#_ftn14]](%%): 1371 1371 1372 1372 ‘urn:sdmx:org.sdmx.infomodel.codelist.Codelist=AG:CL_FREQ(1.0)’ 1373 1373 1374 -if the ,,Codelist,, is referenced from a ruleset scheme belonging to the agency AG, omitting all the optional parts, the abbreviated reference would become simply[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[15~]^^>>path:#_ftn15]](%%):1386 +if the ,,Codelist,, is referenced from a ruleset scheme belonging to the agency AG, omitting all the optional parts, the abbreviated reference would become simply[[(% class="wikiinternallink wikiinternallink" %)^^~[15~]^^>>path:#_ftn15]](%%): 1375 1375 1376 1376 CL_FREQ 1377 1377 ... ... @@ -1381,7 +1381,7 @@ 1381 1381 1382 1382 SECTOR 1383 1383 1384 -For example, the transformation for renaming the component SECTOR of the dataflow DF1 into SEC can be written as[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[16~]^^>>path:#_ftn16]](%%):1396 +For example, the transformation for renaming the component SECTOR of the dataflow DF1 into SEC can be written as[[(% class="wikiinternallink wikiinternallink" %)^^~[16~]^^>>path:#_ftn16]](%%): 1385 1385 1386 1386 ‘DFR(1.0)’ := ‘DF1(1.0)’ [rename SECTOR to SEC] 1387 1387 ... ... @@ -1415,9 +1415,9 @@ 1415 1415 1416 1416 The VTL Rulesets have a signature, in which the Value Domains or the Variables on which the Ruleset is defined are declared, and a body, which contains the rules. 1417 1417 1418 -In the signature, given the mapping between VTL and SDMX better described in the following paragraphs, a reference to a VTL Value Domain becomes a reference to a SDMX Codelist or to a SDMX ConceptScheme (for SDMX measure dimensions), while a reference to a VTL Represented Variable becomes a reference to a SDMX Concept, assuming for it a definite representation[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[17~]^^>>path:#_ftn17]](%%).1430 +In the signature, given the mapping between VTL and SDMX better described in the following paragraphs, a reference to a VTL Value Domain becomes a reference to a SDMX Codelist or to a SDMX ConceptScheme (for SDMX measure dimensions), while a reference to a VTL Represented Variable becomes a reference to a SDMX Concept, assuming for it a definite representation[[(% class="wikiinternallink wikiinternallink" %)^^~[17~]^^>>path:#_ftn17]](%%). 1419 1419 1420 -In general, for referencing SDMX Codelists and Concepts, the conventions described in the previous paragraphs apply. In the Ruleset syntax, the elements that reference SDMX artefacts are called “valueDomain” and “variable” for the Datapoint Rulesets and “ruleValueDomain”, “ruleVariable”, “condValueDomain” “condVariable” for the Hierarchical Rulesets). The syntax of the Ruleset signature allows also to define aliases of the elements above, these aliases are valid only within the specific ruleset definition statement and cannot be mapped to SDMX.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[18~]^^>>path:#_ftn18]](%%)1432 +In general, for referencing SDMX Codelists and Concepts, the conventions described in the previous paragraphs apply. In the Ruleset syntax, the elements that reference SDMX artefacts are called “valueDomain” and “variable” for the Datapoint Rulesets and “ruleValueDomain”, “ruleVariable”, “condValueDomain” “condVariable” for the Hierarchical Rulesets). The syntax of the Ruleset signature allows also to define aliases of the elements above, these aliases are valid only within the specific ruleset definition statement and cannot be mapped to SDMX.[[(% class="wikiinternallink wikiinternallink" %)^^~[18~]^^>>path:#_ftn18]](%%) 1421 1421 1422 1422 In the body of the Rulesets, the Codes and in general all the Values can be written without any other specification, because the artefact which the Values are referred (Codelist, ConceptScheme, Concept) to can be deduced from the Ruleset signature. 1423 1423 ... ... @@ -1431,15 +1431,15 @@ 1431 1431 1432 1432 Every time a SDMX object is referenced in a VTL Transformation as an input operand, there is the need to generate a VTL definition of the object, so that the VTL operations can take place. This can be made starting from the SDMX definition and applying a SDMX-VTL mapping method in the direction from SDMX to VTL. The possible mapping methods from SDMX to VTL are described in the following paragraphs and are conceived to allow the automatic deduction of the VTL definition of the object from the knowledge of the SDMX definition. 1433 1433 1434 -In the opposite direction, every time an object calculated by means of VTL must be treated as a SDMX object (for example for exchanging it through SDMX), there is the need of a SDMX definition of the object, so that the SDMX operations can take place. The SDMX definition is needed for the VTL objects for which a SDMX use is envisaged[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[19~]^^>>path:#_ftn19]](%%).1446 +In the opposite direction, every time an object calculated by means of VTL must be treated as a SDMX object (for example for exchanging it through SDMX), there is the need of a SDMX definition of the object, so that the SDMX operations can take place. The SDMX definition is needed for the VTL objects for which a SDMX use is envisaged[[(% class="wikiinternallink wikiinternallink" %)^^~[19~]^^>>path:#_ftn19]](%%). 1435 1435 1436 1436 The mapping methods from VTL to SDMX are described in the following paragraphs as well, however they do not allow the complete SDMX definition to be automatically deduced from the VTL definition, more than all because the former typically contains additional information in respect to the latter. For example, the definition of a SDMX DSD includes also some mandatory information not available in VTL (like the concept scheme to which the SDMX components refer, the assignmentStatus and attributeRelationship for the DataAttributes and so on). Therefore the mapping methods from VTL to SDMX provide only a general guidance for generating SDMX definitions properly starting from the information available in VTL, independently of how the SDMX definition it is actually generated (manually, automatically or part and part). 1437 1437 1438 1438 === 10.3.2 General mapping of VTL and SDMX data structures === 1439 1439 1440 -This section makes reference to the VTL “Model for data and their structure”[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[20~]^^>>path:#_ftn20]](%%) and the correspondent SDMX “Data Structure Definition”[[(% class="wikiinternallink wikiinternallinkwikiinternallink wikiinternallink" %)^^~[21~]^^>>path:#_ftn21]](%%).1452 +This section makes reference to the VTL “Model for data and their structure”[[(% class="wikiinternallink wikiinternallink" %)^^~[20~]^^>>path:#_ftn20]](%%) and the correspondent SDMX “Data Structure Definition”[[(% class="wikiinternallink wikiinternallink" %)^^~[21~]^^>>path:#_ftn21]](%%). 1441 1441 1442 -The main type of artefact that the VTL can manipulate is the VTL Data Set, which in general is mapped to the SDMX Dataflow. This means that a VTL Transformation, in the SDMX context, expresses the algorithm for calculating a derived Dataflow starting from some already existing Dataflows (either collected or derived).[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[22~]^^>>path:#_ftn22]](%%)1454 +The main type of artefact that the VTL can manipulate is the VTL Data Set, which in general is mapped to the SDMX Dataflow. This means that a VTL Transformation, in the SDMX context, expresses the algorithm for calculating a derived Dataflow starting from some already existing Dataflows (either collected or derived).[[(% class="wikiinternallink wikiinternallink" %)^^~[22~]^^>>path:#_ftn22]](%%) 1443 1443 1444 1444 While the VTL Transformations are defined in term of Dataflow definitions, they are assumed to be executed on instances of such Dataflows, provided at runtime to the VTL engine (the mechanism for identifying the instances to be processed are not part of the VTL specifications and depend on the implementation of the VTL-based systems). As already said, the SDMX Datasets are instances of SDMX Dataflows, therefore a VTL Transformation defined on some SDMX Dataflows can be applied on some corresponding SDMX Datasets. 1445 1445 ... ... @@ -1449,7 +1449,7 @@ 1449 1449 1450 1450 SDMX DimensionComponent can be a Dimension, a TimeDimension or a MeasureDimension. Correspondingly, in the SDMX implementation of the VTL, the VTL Identifiers can be (optionally) distinguished in three sub-classes (Simple Identifier, Time Identifier, Measure Identifier) even if such a distinction is not evidenced in the VTL IM. 1451 1451 1452 -However, a VTL Data Structure can have any number of Identifiers, Measures and Attributes, while a SDMX 2.1 DataStructureDefinition can have any number of Dimensions and DataAttributes but just one PrimaryMeasure[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[23~]^^>>path:#_ftn23]](%%). This is due to a difference between SDMX 2.1 and VTL in the possible representation methods of the data that contain more measures.1464 +However, a VTL Data Structure can have any number of Identifiers, Measures and Attributes, while a SDMX 2.1 DataStructureDefinition can have any number of Dimensions and DataAttributes but just one PrimaryMeasure[[(% class="wikiinternallink wikiinternallink" %)^^~[23~]^^>>path:#_ftn23]](%%). This is due to a difference between SDMX 2.1 and VTL in the possible representation methods of the data that contain more measures. 1453 1453 1454 1454 As for SDMX, because the data structure cannot contain more than one measure component (i.e., the primaryMeasure), the representation of data having more measures is possible only by means of a particular dimension, called MeasureDimension, which is aimed at containing the name of the measure concepts, so that for each observation the value contained in the PrimaryMeasure component is the value of the measure concept reported in the MeasureDimension component. 1455 1455 ... ... @@ -1539,7 +1539,7 @@ 1539 1539 1540 1540 This mapping method cannot be applied for SDMX 2.1 if the VTL data structure has more than one measure component, given that the SDMX 2.1 DataStructureDefinition allows just one measure component (the 1541 1541 1542 -PrimaryMeasure). In this case it becomes mandatory to specify a different 1958 mapping method through the VtlMappingScheme and VtlDataflowMapping 1959 classes.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[24~]^^>>path:#_ftn24]](%%)1554 +PrimaryMeasure). In this case it becomes mandatory to specify a different 1958 mapping method through the VtlMappingScheme and VtlDataflowMapping 1959 classes.[[(% class="wikiinternallink wikiinternallink" %)^^~[24~]^^>>path:#_ftn24]](%%) 1543 1543 1544 1544 1960 Please note that the VTL measures can have any name while in SDMX 2.1 the 1961 MeasureComponent has the mandatory name “obs_value”, therefore the name of the VTL measure name must become “obs_value” in SDMX 2.1. 1545 1545 ... ... @@ -1656,15 +1656,15 @@ 1656 1656 1657 1657 The VtlMappingScheme is a container for zero or more VtlDataflowMapping (besides possible mappings to artefacts other than dataflows). 1658 1658 1659 -=== 10.3.6 Mapping dataflow subsets to distinct VTL data sets[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^**~[25~]**^^>>path:#_ftn25]](%%) ===1671 +=== 10.3.6 Mapping dataflow subsets to distinct VTL data sets[[(% class="wikiinternallink wikiinternallink" %)^^**~[25~]**^^>>path:#_ftn25]](%%) === 1660 1660 1661 1661 Until now it as been assumed to map one SMDX Dataflow to one VTL dataset and vice-versa. This mapping one-to-one is not mandatory according to VTL because a VTL data set is meant to be a set of observations (data points) on a logical plane, having the same logical data structure and the same general meaning, independently of the possible physical representation or storage (see VTL 2.0 User Manual page 1662 1662 1663 1663 24), therefore a SDMX Dataflow can be seen either as a unique set of data observations (corresponding to one VTL data set) or as the union of many sets of data observations (each one corresponding to a distinct VTL data set). 1664 1664 1665 -As a matter of fact, in some cases it can be useful to define VTL operations involving definite parts of a SDMX Dataflow instead than the whole.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[26~]^^>>path:#_ftn26]](%%)1677 +As a matter of fact, in some cases it can be useful to define VTL operations involving definite parts of a SDMX Dataflow instead than the whole.[[(% class="wikiinternallink wikiinternallink" %)^^~[26~]^^>>path:#_ftn26]](%%) 1666 1666 1667 -Therefore, in order to make the coding of VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL data sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[27~]^^>>path:#_ftn27]](%%)1679 +Therefore, in order to make the coding of VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL data sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.[[(% class="wikiinternallink wikiinternallink" %)^^~[27~]^^>>path:#_ftn27]](%%) 1668 1668 1669 1669 Given a SDMX Dataflow and some predefined Dimensions of its 1670 1670 ... ... @@ -1676,14 +1676,14 @@ 1676 1676 1677 1677 In practice, this kind mapping is obtained like follows: 1678 1678 1679 -* For a given SDMX dataflow, the user (VTL definer) declares the dimension components on which the mapping will be based, in a given order.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[28~]^^>>path:#_ftn28]](%%) Following the example above, imagine that the user declares the dimensions INDICATOR and COUNTRY.1691 +* For a given SDMX dataflow, the user (VTL definer) declares the dimension components on which the mapping will be based, in a given order.[[(% class="wikiinternallink wikiinternallink" %)^^~[28~]^^>>path:#_ftn28]](%%) Following the example above, imagine that the user declares the dimensions INDICATOR and COUNTRY. 1680 1680 * The VTL dataset is given a name using a special notation also called “ordered concatenation” and composed of the following parts: 1681 1681 ** The reference to the SDMX dataflow (expressed according to the rules described in the previous paragraphs, i.e. URN, abbreviated 1682 1682 1683 -URN or another alias); for example DF(1.0); o a slash (“/”) as a separator; [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[29~]^^>>path:#_ftn29]]1695 +URN or another alias); for example DF(1.0); o a slash (“/”) as a separator; [[(% class="wikiinternallink wikiinternallink" %)^^~[29~]^^>>path:#_ftn29]] 1684 1684 1685 1685 * 1686 -** The reference to a specific part of the SDMX dataflow above, expressed as the concatenation of the values that the SDMX dimensions declared above must have, separated by dots (“.”) and written in the order in which these dimensions are defined[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[30~]^^>>path:#_ftn30]](%%) . For example POPULATION.USA would mean that such a VTL dataset is mapped to the SDMX observations for which the dimension //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.1698 +** The reference to a specific part of the SDMX dataflow above, expressed as the concatenation of the values that the SDMX dimensions declared above must have, separated by dots (“.”) and written in the order in which these dimensions are defined[[(% class="wikiinternallink wikiinternallink" %)^^~[30~]^^>>path:#_ftn30]](%%) . For example POPULATION.USA would mean that such a VTL dataset is mapped to the SDMX observations for which the dimension //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA. 1687 1687 1688 1688 In the VTL transformations, this kind of dataset name must be referenced between single quotes because the slash (“/”) is not a regular character according to the VTL rules. 1689 1689 ... ... @@ -1701,7 +1701,7 @@ 1701 1701 1702 1702 Let us now analyse the different meaning of this kind of mapping in the two mapping directions, i.e. from SDMX to VTL and from VTL to SDMX. 1703 1703 1704 -As already said, the mapping from SDMX to VTL happens when the VTL datasets are operand of VTL transformations, instead the mapping from VTL to SDMX happens when the VTL datasets are result of VTL transformations[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[31~]^^>>path:#_ftn31]](%%) and need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.1716 +As already said, the mapping from SDMX to VTL happens when the VTL datasets are operand of VTL transformations, instead the mapping from VTL to SDMX happens when the VTL datasets are result of VTL transformations[[(% class="wikiinternallink wikiinternallink" %)^^~[31~]^^>>path:#_ftn31]](%%) and need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively. 1705 1705 1706 1706 First, let us see what happens in the mapping direction from SDMX to VTL, i.e. when parts of a SDMX dataflow (e.g. DF1(1.0)) need to be mapped to distinct VTL datasets that are operand of some VTL transformations. 1707 1707 ... ... @@ -1711,7 +1711,7 @@ 1711 1711 1712 1712 //COUNTRYvalue//. For example, the VTL dataset ‘DF1(1.0)/POPULATION.USA’ would contain all the observations of DF1(1.0) having INDICATOR = POPULATION and COUNTRY = USA. 1713 1713 1714 -In order to obtain the data structure of these VTL datasets from the SDMX one, it is assumed that the SDMX dimensions on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL datasets[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[32~]^^>>path:#_ftn32]](%%). After that, the mapping method from SDMX to VTL specified for the dataflow DF1(1.0) is applied (i.e. basic, pivot …).1726 +In order to obtain the data structure of these VTL datasets from the SDMX one, it is assumed that the SDMX dimensions on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL datasets[[(% class="wikiinternallink wikiinternallink" %)^^~[32~]^^>>path:#_ftn32]](%%). After that, the mapping method from SDMX to VTL specified for the dataflow DF1(1.0) is applied (i.e. basic, pivot …). 1715 1715 1716 1716 In the example above, for all the datasets of the kind 1717 1717 ... ... @@ -1731,7 +1731,7 @@ 1731 1731 1732 1732 … … … 1733 1733 1734 -In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX dataflow to different VTL datasets in the direction from SDMX to VTL through the ordered concatenation notation is equivalent to a proper use of the operator “**sub**” on such a dataflow. [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[33~]^^>>path:#_ftn33]]1746 +In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX dataflow to different VTL datasets in the direction from SDMX to VTL through the ordered concatenation notation is equivalent to a proper use of the operator “**sub**” on such a dataflow. [[(% class="wikiinternallink wikiinternallink" %)^^~[33~]^^>>path:#_ftn33]] 1735 1735 1736 1736 In the direction from SDMX to VTL it is allowed to omit the value of one or more Dimensions on which the mapping is based, but maintaining all the separating dots (therefore it may happen to find two or more consecutive dots and dots in the beginning or in the end). The absence of value means that for the corresponding Dimension all the values are kept and the Dimension is not dropped. 1737 1737 ... ... @@ -1754,12 +1754,12 @@ 1754 1754 1755 1755 For example, let us assume that the VTL programmer wants to calculate the SDMX dataflow DF2(1.0) having the Dimensions TIME_PERIOD, INDICATOR, and COUNTRY and that such a programmer finds it convenient to calculate separately the parts of DF2(1.0) that have different combinations of values for INDICATOR and COUNTRY: 1756 1756 1757 -* each part is calculated as a VTL derived dataset, result of a dedicated VTL transformation; [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[34~]^^>>path:#_ftn34]](%%)1758 -* the data structure of all these VTL datasets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[35~]^^>>path:#_ftn35]]1769 +* each part is calculated as a VTL derived dataset, result of a dedicated VTL transformation; [[(% class="wikiinternallink wikiinternallink" %)^^~[34~]^^>>path:#_ftn34]](%%) 1770 +* the data structure of all these VTL datasets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.[[(% class="wikiinternallink wikiinternallink" %)^^~[35~]^^>>path:#_ftn35]] 1759 1759 1760 -Under these hypothesis, such derived VTL datasets can be mapped to DF2(1.0) by declaring the Dimensions INDICATOR and COUNTRY as mapping dimensions[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[36~]^^>>path:#_ftn36]](%%).1772 +Under these hypothesis, such derived VTL datasets can be mapped to DF2(1.0) by declaring the Dimensions INDICATOR and COUNTRY as mapping dimensions[[(% class="wikiinternallink wikiinternallink" %)^^~[36~]^^>>path:#_ftn36]](%%). 1761 1761 1762 -The corresponding VTL transformations, assuming that the result needs to be persistent, would be of this kind:^^ ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[37~]^^>>path:#_ftn37]]1774 +The corresponding VTL transformations, assuming that the result needs to be persistent, would be of this kind:^^ ^^[[(% class="wikiinternallink wikiinternallink" %)^^~[37~]^^>>path:#_ftn37]] 1763 1763 1764 1764 ‘DF2(1.0)///INDICATORvalue//.//COUNTRYvalue//’ <- expression 1765 1765 ... ... @@ -1826,9 +1826,9 @@ 1826 1826 1827 1827 …); 1828 1828 1829 -In other words, starting from the datasets explicitly calculated through VTL (in the example ‘DF2(1.0)/GDPPERCAPITA.USA’ and so on), the first step consists in calculating other (non-persistent) VTL datasets (in the example DF2bis_GDPPERCAPITA_USA and so on) by adding the identifiers INDICATOR and COUNTRY with the desired values (//INDICATORvalue// and //COUNTRYvalue)//. Finally, all these non-persistent data sets are united and give the final result DF2(1.0)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[38~]^^>>path:#_ftn38]](%%), which can be mapped one-to-one to the homonymous SDMX dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.1841 +In other words, starting from the datasets explicitly calculated through VTL (in the example ‘DF2(1.0)/GDPPERCAPITA.USA’ and so on), the first step consists in calculating other (non-persistent) VTL datasets (in the example DF2bis_GDPPERCAPITA_USA and so on) by adding the identifiers INDICATOR and COUNTRY with the desired values (//INDICATORvalue// and //COUNTRYvalue)//. Finally, all these non-persistent data sets are united and give the final result DF2(1.0)[[(% class="wikiinternallink wikiinternallink" %)^^~[38~]^^>>path:#_ftn38]](%%), which can be mapped one-to-one to the homonymous SDMX dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY. 1830 1830 1831 -Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets. [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[39~]^^>>path:#_ftn39]](%%)[[(% class="wikiinternallink wikiinternallinkwikiinternallink wikiinternallink" %)^^~[40~]^^>>path:#_ftn40]]1843 +Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets. [[(% class="wikiinternallink wikiinternallink" %)^^~[39~]^^>>path:#_ftn39]](%%)[[(% class="wikiinternallink wikiinternallink" %)^^~[40~]^^>>path:#_ftn40]] 1832 1832 1833 1833 It is worth noting that in the direction from VTL to SDMX it is mandatory to specify the value for every Dimension on which the mapping is based (in other word, in the name of the calculated VTL dataset is not possible to omit the value of some of the Dimensions). 1834 1834 ... ... @@ -1877,7 +1877,7 @@ 1877 1877 1878 1878 Domain) is not identifiable. As a consequence, the definition of the VTL rulesets, which in VTL can refer either to enumerated or non-enumerated value domains, in SDMX can refer only to enumerated Value Domains (i.e. to SDMX Codelists). 1879 1879 1880 -As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[41~]^^>>path:#_ftn41]](%%), while the SDMX Concepts can have different Representations in different DataStructures.[[(% class="wikiinternallink wikiinternallinkwikiinternallink wikiinternallink" %)^^~[42~]^^>>path:#_ftn42]](%%) This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.1892 +As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear[[(% class="wikiinternallink wikiinternallink" %)^^~[41~]^^>>path:#_ftn41]](%%), while the SDMX Concepts can have different Representations in different DataStructures.[[(% class="wikiinternallink wikiinternallink" %)^^~[42~]^^>>path:#_ftn42]](%%) This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has. 1881 1881 1882 1882 Therefore, it is important to be aware that some VTL operations (for example the binary operations at data set level) are consistent only if the components having the same names in the operated VTL data sets have also the same representation (i.e. the same Value Domain as for VTL). For example, it is possible to obtain correct results from the VTL expression 1883 1883 ... ... @@ -2222,7 +2222,7 @@ 2222 2222 |N|fixed number of digits used in the preceding textual representation of the month or the day 2223 2223 | | 2224 2224 2225 -The default conversion, either standard or customized, can be used to deduce automatically the representation of the components of the result of a VTL transformation. In alternative, the representation of the resulting SDMX Dataflow can be given explicitly by providing its DataStructureDefinition. In other words, the representation specified in the DSD, if available, overrides any default conversion[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[43~]^^>>path:#_ftn43]](%%).2237 +The default conversion, either standard or customized, can be used to deduce automatically the representation of the components of the result of a VTL transformation. In alternative, the representation of the resulting SDMX Dataflow can be given explicitly by providing its DataStructureDefinition. In other words, the representation specified in the DSD, if available, overrides any default conversion[[(% class="wikiinternallink wikiinternallink" %)^^~[43~]^^>>path:#_ftn43]](%%). 2226 2226 2227 2227 === 10.4.5 Null Values === 2228 2228